Description Usage Arguments Details Value Author(s) References See Also Examples
ogaur
can be used to find the Ordinary Generalized Almost Unbiased Ridge Estimated values and corresponding scalar Mean Square Error (MSE) value in the linear model. Further the variation of MSE can be shown graphically.
1 |
formula |
in this section interested model should be given. This should be given as a |
k |
a single numeric value or a vector of set of numeric values. See ‘Example’. |
data |
an optional data frame, list or environment containing the variables in the model. If not found in |
na.action |
if the dataset contain |
... |
currently disregarded. |
Since formula has an implied intercept term, use either y ~ x - 1
or y ~ 0 + x
to remove the intercept.
Use plot
so as to obtained the variation of scalar MSE values graphically. See ‘Examples’.
If k
is a single numeric values then ogaur
returns the Ordinary Generalized Almost Unbiased Ridge Estimated values, standard error values, t statistic values, p value and corresponding scalar MSE value.
If k
is a vector of set of numeric values then ogaur
returns all the scalar MSE values and corresponding parameter values of Ordinary Generalized Almost Unbiased Ridge Estimator.
P.Wijekoon, A.Dissanayake
Arumairajan, S. and Wijekoon, P. (2015) ] Optimal Generalized Biased Estimator in Linear Regression Model in Open Journal of Statistics, pp. 403–411
Akdeniz, F. and Erol, H. (2003) Mean Squared Error Matrix Comparisons of Some Biased Estimators in Linear Regression in Communications in Statistics - Theory and Methods, volume 32 DOI:10.1081/STA-120025385
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | ## Portland cement data set is used.
data(pcd)
k<-0.05
ogaur(Y~X1+X2+X3+X4-1,k,data=pcd)
# Model without the intercept is considered.
## To obtain the variation of MSE of
# Ordinary Generalized Almost Unbiased Ridge Estimator.
data(pcd)
k<-c(0:10/10)
plot(ogaur(Y~X1+X2+X3+X4-1,k,data=pcd),
main=c("Plot of MSE of Ordinary Generalized
Almost Unbiased Ridge Estimator"),type="b",
cex.lab=0.6,adj=1,cex.axis=0.6,cex.main=1,las=1,lty=3,cex=0.6)
mseval<-data.frame(ogaur(Y~X1+X2+X3+X4-1,k,data=pcd))
smse<-mseval[order(mseval[,2]),]
points(smse[1,],pch=16,cex=0.6)
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